Underwater image restoration using deep encoder–decoder network with symmetric skip connections

Underwater images get degraded for a variety of naturally occurring attributes like haze, suspended particles, light scattering and water types. The primary cause of the degradation is underwater light attenuation that varies with wavelength, unlike the uniform attenuation that occurs in-air. In this paper, we propose an end-to-end deep convolutional neural network architecture to restore the underwater images and improve their visual perception. The encoder learns to encode the degraded image to a lower-dimensional feature map, while the decoder learns to restore the image to a degradation-free form. This is achieved due to the utilization of symmetric skip connections between the encoder–decoder blocks for the propagation of feature maps to improve the sharpness of the restored image and prevent the loss of details caused by the convolutions. We exhaustively evaluate the performance of our network both qualitatively and quantitatively on standard datasets, and the effectiveness of our network is demonstrated with existing methods of underwater image restoration and enhancement techniques.